With technological advancements and decreasing costs, vast amounts of medical imaging datasets are becoming available for clinical and scientific interpretation. However, annotation of imaging data is difficult due to its size and complexity, costly as it currently requires the time and expertise of a limited set of trained readers, and subjective as the annotations can vary based on the reader. As a result, large imaging datasets may be unlabelled; that is, there is no annotation of the scan of interest to make it recognizable in some way, such as normal or diseased, disease severity, or type of disease. In addition, annotations from visual assessment or scoring may not adequately capture the complex patterns observed in the data. In either case, objective, data-driven methods that can intelligibly and trustworthily assign labels to medical imaging datasets are needed. In this work, we extend an existing deep clustering framework [1]}, [2]}, [3]} by coupling it with our novel cluster activation mapping technique [4]} to learn the best clusters of scans from medical imaging datasets and understand the reason for a scan's cluster assignment by providing a visual representation of cluster importance.
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